An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data

EasyChair Preprint 6379

Abstract

Anomaly detection is critical in various fields, including intrusion detection, health monitoring, fault diagnosis, and sensor network event detection. The isolation forest (or iForest) approach is a well-known technique for detecting anomalies. It is, however, ineffective when dealing with dynamic streaming data, which is becoming increasingly prevalent in a wide variety of application areas these days. In this work, We proposed an efficient iForest based approach for anomaly detection using cube sampling that is effective on streaming data. Cube sampling is used in the initial stage to choose nearly balanced samples with equal or unequal inclusion probability, significantly reducing storage requirements while preserving efficiency. Following that, the data's streaming nature is addressed by a sliding window technique that generates consecutive chunks of data for systematic processing. The proposed approach is equally successful at detecting anomalies as existing state-of-the-art approaches while requiring significantly less storage and time complexity. We undertake empirical evaluations of the proposed approach using standard datasets and demonstrate that it outperforms traditional approaches in terms of Area Under the ROC Curve (AUC-ROC) and can handle high-dimensional streaming data.

Keyphrases: Cube Sampling, Isolation Forest, anomaly detection, sliding window, streaming data

@booklet{EasyChair:6379,
  author    = {Seemandhar Jain and Prarthi Jain and Abhishek Srivastava},
  title     = {An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data},
  howpublished = {EasyChair Preprint 6379},
  year      = {EasyChair, 2021}}